Search · four archives
Search · four archives
27 papers · ranked by Valyu relevance
Johann Faouzi, Olivier Colliot
In this chapter, we present the main classic machine learning methodss. A large part of the chapter is devoted to supervised learning techniques for classification and regression, including nearest-neighbor methods, linear and logistic regressions, support vector machines and tree-based algorithms. We also describe the…
Joram Soch, Carsten Allefeld
We propose the statistical modelling approach to supervised learning (i.e. predicting labels from features) as an alternative to algorithmic machine learning (ML). The approach is demonstrated by employing a multivariate general linear model (MGLM) describing the effects of labels on features, possibly accounting for…
Zeinab Noroozi, Azam Orooji, Leila Erfannia
The present study examines the role of feature selection methods in optimizing machine learning algorithms for predicting heart disease. The Cleveland Heart disease dataset with sixteen feature selection techniques in three categories of filter, wrapper, and evolutionary were used. Then seven algorithms Bayes net…
Paola Patricia Ariza-Colpas, Enrico Vicario, Ana Isabel Oviedo-Carrascal, Shariq Butt Aziz + 7 more
The Assisted Living Environments Research Area-AAL (Ambient Assisted Living), focuses on generating innovative technology, products, and services to assist, medical care and rehabilitation to older adults, to increase the time in which these people can live. independently, whether they suffer from neurodegenerative…
Samuel K. Sheppard, Nicolas Arning, David W. Eyre, Daniel J. Wilson
The availability of large genome datasets has changed the microbiology research landscape. Analyzing such data requires computationally demanding analyses, and new approaches have come from different data analysis philosophies. Machine learning and statistical inference have overlapping knowledge discovery aims and…
Mohammed G. Sghaireen, Yazan Al-Smadi, Ahmad Al-Qerem, Kumar Chandan Srivastava + 5 more
'Kumar Chandan Srivastava' 'Kiran Kumar Ganji' 'Mohammad Khursheed Alam' 'Shadi Nashwan' 'Yousef Khader' 'Simona Bungau'] Metabolic syndrome (MetS) is a cluster of risk factors including hypertension, hyperglycemia, dyslipidemia, and abdominal obesity. Metabolism-related risk factors include diabetes and heart disease.…
Joshua P. Jahner, C. Alex Buerkle, Dustin G. Gannon, Eliza M. Grames + 11 more
The proliferation of biological data with large numbers of samples and many dimensions is kindling hope that life scientists will be able to fit statistical and machine learning models that are highly predictive and interpretable. However, large biological data sets are commonly burdened with an inherent trade-off…
Giovanni Cerulli
We present two related Stata modules, r ml stata and c ml stata, for fitting popular Machine Learning (ML) methods both in a regression and a classification setting. Using the recent Stata/Python integration platform (sfi) of Stata 16, these commands provide hyper-parameters' optimal tuning via K-fold cross-validation…
Marie Salditt, Theresa Eckes, Steffen Nestler
Psychotherapy has been proven to be effective on average, though patients respond very differently to treatment. Understanding which characteristics are associated with treatment effect heterogeneity can help to customize therapy to the individual patient. In this tutorial, we describe different meta-learners, which…
Aryan Yazdanpanah, Michael Chong Wang, Ethan Trepka, Marissa Benz + 1 more
Natural environments are abundant with patterns and regularities. These regularities can be captured through statistical learning, which strongly influences perception, memory, and other cognitive functions. By combining a sequence-prediction task with an orthogonal multidimensional reward learning task, we tested…
Elena Menichini, Quentin Pajot-Moric, Ryan Low, Victor Pedrosa + 5 more
Animals must exploit environmental regularities to make adaptive decisions, yet the learning algorithms that enabels this flexibility remain unclear. A central question across neuroscience, cognitive science, and machine learning, is whether learning relies on generative or discriminative strategies. Generative…
Nusrat Jahan Prottasha, Saydul Akbar Murad, Abu Jafar Md Muzahid, Masud Rana + 4 more
'Masud Rana' 'Md. Kowsher' 'Apurba Adhikary' 'S.K. Biswas' 'Anupam Kumar Bairagi'] Abstract—Machine learning is the study of computer algorithms that can automatically improve based on data and experience. Machine learning algorithms build a model from sample data, called training data, to make predictions or judgments…
Angela An, James Jin Kang
In the healthcare system, patients are required to use wearable devices for the remote data collection and real-time monitoring of health data and the status of health conditions. This adoption of wearables results in a significant increase in the volume of data that is collected and transmitted. As the devices are run…
Pierre Bongrand, Binh P. Nguyen, Fei Guo
During the last decade, artificial intelligence (AI) was applied to nearly all domains of human activity, including scientific research. It is thus warranted to ask whether AI thinking should be durably involved in biomedical research. This problem was addressed by examining three complementary questions (i) What are…
Mohammad H. Zhoolideh Haghighi
Classification is a popular task in the field of Machine Learning (ML) and Artificial Intelligence (AI), and it happens when outputs are categorical variables. There are a wide variety of models that attempts to draw some conclusions from observed values, so classification algorithms predict categorical class labels…
Jaouhar Fattahi
Digital Forensics: a Review Authors: ['Jaouhar Fattahi'] In the paced realms of cybersecurity and digital forensics machine learning (ML) and deep learning (DL) have emerged as game changing technologies that introduce methods to identify stop and analyze cyber risks. This review presents an overview of the ML and DL…
Narjice Chafai, Ichrak Hayah, Isidore Houaga, Bouabid Badaoui
The advent of modern genotyping technologies has revolutionized genomic selection in animal breeding. Large marker datasets have shown several drawbacks for traditional genomic prediction methods in terms of flexibility, accuracy, and computational power. Recently, the application of machine learning models in animal…
Zongben Xu, Jun Shu, Deyu Meng
This paper introduces a ‘simulating learning methodology’ (SLeM) approach for the learning methodology determination in general and for Auto6 ML in particular, and reports the SLeM framework, approaches, algorithms and applications.
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Matthew J. K. Vince, Kristin A. Hughes, Anastasiya Buzuk, Deborah L. Perlstein + 2 more
Machine learning (ML) is rapidly gaining traction in many areas of experimental molecular science for elucidating relationships and patterns in large or complex data sets. Historically, ML was largely the preserve of those with specialized training in fields such as statistics or cheminformatics. Increasingly, however…
Francisco A. Rodrigues
– Machine learning is a rapidly growing field with the potential to revolutionize many areas of science, including physics. This review provides a brief overview of machine learning in physics, covering the main concepts of supervised, unsupervised, and reinforcement learning, as well as more specialized topics such as…
Authors not listed
Solubility is critical in drug discovery and development, as it significantly influences a medication's bioavailability and therapeutic efficacy. Understanding solubility at the early stages of drug discovery is essential for minimizing resource consumption and enhancing the likelihood of clinical success via…
Muhammad Hanzla, Abdul Rehman Shinwari
Machine Learning (ML) can be defined as a class of Artificial Intelligence for automated data analysis, which is capable of detecting patterns in data. The extracted patterns can be used to predict un-known data or to assist in decision-making processes under uncertainty. Recent advances in experimental and…
Authors not listed
We developed OpenStats, a user-friendly web application that brings the power of the R language to researchers through a high-level interface and broad support for statistical methods such as t-tests and ANOVA. OpenStats was integrated into our electronic lab notebook Chemotion ELN via its third-party API, enabling…
Authors not listed
Quantitative Structure Activity Relationship (QSAR) remains an effective tool for early-stage chemical modelling and virtual screening in drug design. The advancements in this field are led by two core paradigms, 1) descriptor engineering, where complex fixed-length vectors of compounds are generated and conventional…
Husam Abdulnabi, J. Timothy Westwood
A quantitative measurement can have variation, referred to here as measurement variation, which is a probability distribution. Machine Learning models typically produce a prediction corresponding to the mode of the measurement variation. The Deviation Error is a novel metric, described here, to assess predictions that…
Authors not listed
Quantitative Structure-Activity Relationship (QSAR) modeling is a pillar of computational drug discovery. However, standard machine learning (ML) models are often confounded by the high-dimensional and intensely correlated nature of molecular descriptors. A model may identify a "bulk" property (e.g., molecular weight)…